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Automatic classification of plant diseases at an early stage is vital for precision crop protection. Our aim was to identify sugar beet leaves inoculated with Cercospora beticola before symptoms are visible. Therefore hyperspectral reflection between 400 and 1050 nm was observed. Relevant wavelengths have to be found in order to implement practical sensor systems with reduced development costs. The main contribution of this study is to identify a minimal subset which is sufficient for separating healthy and inoculated leaves. The heuristic of Hall which analyses the relevance of a feature subset considering the intercorrelation among the features was applied. In order to select a good subset in a reasonable amount of time a genetic algorithm was used. This way enabled a subset of only seven out of 462 wavelengths, which nevertheless enabled us to identify low disease severity ≤ 5% with a classification accuracy of 84.3%. Disease severity above 5% was classified with 99.8%.